High Dimensional Locally-Linear Mapping
EM Algorithm for Block diagonal Gaussian Locally Linear Mapping
Perform EM algorithm for fitting a Gaussian mixture model (GMM)
EM Algorithm for Gaussian Locally Linear Mapping
Inverse Mapping from gllim or bllim parameters
A proposition of function to process high dimensional data before runn...
EM Algorithm for Student Locally Linear Mapping
Inverse Mapping from sllim parameters
High Dimensional Locally-Linear Mapping
Provides a tool for non linear mapping (non linear regression) using a mixture of regression model and an inverse regression strategy. The methods include the GLLiM model (see Deleforge et al (2015) <DOI:10.1007/s11222-014-9461-5>) based on Gaussian mixtures and a robust version of GLLiM, named SLLiM (see Perthame et al (2016) <DOI:10.1016/j.jmva.2017.09.009>) based on a mixture of Generalized Student distributions. The methods also include BLLiM (see Devijver et al (2017) <arXiv:1701.07899>) which is an extension of GLLiM with a sparse block diagonal structure for large covariance matrices (particularly interesting for transcriptomic data).